Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems

Abstract

When we face real problems using computational resources, we understand that it is common to find combinatorial problems in binary domains. Moreover, we have to take into account a large number of possible candidate solutions, since these can be numerous and make it complicated for classical algorithmic techniques to address them. When this happens, in most cases, it becomes a problem due to the high resource cost they generate, so it is of utmost importance to solve these problems efficiently. To cope with this problem, we can apply other methods, such as metaheuristics. There are some metaheuristics that allow operation in discrete search spaces; however, in the case of continuous swarm intelligence metaheuristics, it is necessary to adapt them to operate in discrete domains. To perform this adaptation, it is necessary to use a binary scheme to take advantage of the original moves of the metaheuristics designed for continuous problems. In this work, we propose to hybridize the whale optimization algorithm metaheuristic with the Q-learning reinforcement learning technique, which we call (the QBWOA). By using this technique, we are able to realize an smart and fully online binarization scheme selector, the results have been statistically promising thanks to the respective tables and graphs.